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关键词： Ciphers   Cryptography   Quantum computing   Security   Encryption   Reviews   Quantum cryptography   Computer science   Transforms   Systematics   Symmetric encryption   block ciphers   key size   advanced encryption standard (AES)   operation mode   Feistel networks   Grover's algorithm   Simon's algorithm   post-quantum cryptography   quantum computing   quantum cryptanalysis   lightweight block cipher   time complexity   quantum circuit implementation  

摘要： Recent advances in cryptography aim to address threats posed by quantum computers. However, these advances have focused disproportionately on asymmetric schemes, while symmetric primitives like block ciphers have received less attention. The common assumption is that doubling block cipher key sizes provides adequate quantum resistance, but this oversimplifies the challenge. This paper provides a systematic examination of block cipher security in the quantum computing era. The paper first establishes a formal framework for understanding block cipher security, covering their mathematical foundations and essential security properties. Building on this foundation, we examine how quantum computing fundamentally challenges these security assumptions, going beyond the simple impact on key lengths. Our review of recent research reveals that quantum attacks can exploit structural vulnerabilities in block ciphers and their modes of operation, requiring more comprehensive defenses than key length adjustments alone. We analyze emerging approaches in quantum-resistant design and identify critical areas requiring further research to ensure block cipher security in a post-quantum world.

关键词： Uncertainty   Noise measurement   Training   Cross modal retrieval   Semantics   Training data   Noise   Computer science   Robustness   Electronic mail   Cross-modal retrieval   partially mismatched pairs   uncertainty guided division   uncertainty self-correction   trusted margin loss  

摘要： The manual annotation of perfectly aligned labels for cross-modal retrieval (CMR) is incredibly labor-intensive. As an alternative, the collection of co-occurring data pairs from the Internet is a remarkably cost-effective way, but which, inevitably induces the Partially Mismatched Pairs (PMPs) and therefore significantly degrades the retrieval performance without particular treatment. Previous efforts often utilize the pair-wise similarity to filter out the mismatched pairs, and such operation is highly sensitive to mismatched or ambiguous data and thus leads to sub-optimal performance. To alleviate these concerns, we propose an efficient approach, termed UCPM, i.e., Uncertainty-guided Cross-modal retrieval with Partially Mismatched pairs, which can significantly reduce the adverse impact of mismatched data pairs. Specifically, a novel Uncertainty Guided Division (UGD) strategy is sophisticatedly designed to divide the corrupted training data into confident matched (clean), easily-identifiable mismatched (noisy) and hardly-determined hard subsets, and the derived uncertainty can simultaneously guide the informative pair learning while reducing the negative impact of potential mismatched pairs. Meanwhile, an effective Uncertainty Self-Correction (USC) mechanism is concurrently presented to accurately identify and rectify the fluctuated uncertainty during the training process, which further improves the stability and reliability of the estimated uncertainty. Besides, a Trusted Margin Loss (TML) is newly designed to enhance the discriminability between those hard pairs, by dynamically adjusting their soft margins to amplify the positive contributions of matched pairs while suppressing the negative impacts of mismatched pairs. Extensive experiments on three widely-used benchmark datasets, verify the effectiveness and reliability of UCPM compared with the existing SOTA approaches, and significantly improve the robustness in both synthetic and real-world PMPs. The code i

关键词： Genetic algorithms   Adaptive learning   Real-time systems   Heuristic algorithms   Education   Adaptation models   Recommender systems   Adaptive systems   Systematic literature review   Computer science   content recommendation   genetic algorithm   personalized education   educational technology   optimization   learner engagement   performance metrics   real-time adaptation   artificial intelligence  

摘要： As educational technologies mature at a growing rate, effective models of instructional delivery systems that can deliver learning experiences based on the individual learner's learning style have also been created. One of the core elements of such systems is the ability to recommend the right content that improves the learner's experience. The adaptative learning environment and content recommendation with the help of genetic algorithms are investigated in this paper. GA, based on the principles of Natural Selection, provides stability-based search and solving capacity needed to tackle the inherent messiness and diversity of PBL. In our approach, each learner's context information, as well as performance metrics, preferences and learning goals, are dynamic and fed into the GA. The algorithm genetically generates a random vote of potential content sequences as the initial population that improves through creation by selection, crossover, or mutation. The fitness function assesses an individual sequence in terms of its conformity to the learner's profile and, thus, helps to increase an LMS usage rate, select appropriate content, and achieve targeted educational objectives. The proposed system is an application of GA that constantly updates feedback from the learner's generated performance data and adjusts the recommended content in real time. This dynamic creates a positive learning environment capable of enhancing learner interaction and improving learning achievements. When the proposed GA-based solution is applied in a specific case with an instructional design within an online learning context, enhancement in learner satisfaction and performance are substantial, sufficiently proving the approach's applicability. Thus, this study expands the existing literature on adaptive learning technologies by including the usage of GA in education. Future work will extend this by investigating how GAs can be mixed with other AI approaches, GA applicability to larger datasets,

关键词： Correlation   Social networking (online)   Feature extraction   Emotion recognition   Blogs   Deep learning   Context modeling   Computer science   Computational modeling   Accuracy   Deep learning   multilabel emotion detection   social correlation   social media platforms   temporal correlation   user interaction  

摘要： Emotions are closely tied to human behavior and play a critical role in daily life. With the widespread use of social media, individuals frequently express their feelings online, making emotion extraction from social networks an active area of research. This has applications in domains such as patient emotion monitoring, emotional text-to-speech synthesis, and empathetic chatbots. While numerous studies have focused on single-emotion detection, limited work has addressed the simultaneous identification of multiple coexisting emotions. These emotions are often influenced by contextual factors, including past emotional states and social interactions. Leveraging such dependencies can significantly enhance emotion detection performance. This study proposes a fast, accurate, and robust method for predicting future emotions based on current emotional states, while also identifying interrelated emotions present within social media posts. We annotate a public dataset with multilabel emotion tags and model contextual dependencies, both temporal and social, using a novel Stacked Long Short-Term Memory (LSTM) architecture. The model incorporates encoder-decoder layers to capture short- and long-term dependencies among emotions and employs a time-distributed dense layer to differentiate features associated with each emotion. Our experiments show significant improvements in capturing contextual correlations among multiple emotions. The proposed model achieved a micro F1-score of 0.50 on the GoEmotions dataset and demonstrated comparable performance to transformer-based models on the DepressionEmo dataset, while requiring fewer computational resources. These findings underscore the effectiveness and efficiency of our approach in modeling complex emotional dynamics across social networks.

关键词： Decision making   Uncertainty   Fuzzy sets   Transportation   Adaptation models   Urban areas   Computer science   Complexity theory   Real-time systems   Medical services   q-rung picture fuzzy hypersoft set (q-RPFHSS)   consumer electronics-enabled intelligent transportation system   Schweizer-Sklar (SS) aggregation operators   multi-criteria decision-making  

摘要： The development of intelligent transportation systems (ITS), supported by consumer electronics, is important for modern urban mobility. However, to evaluate these systems, complex, uncertain and interrelated factors, such as technological feasibility, economic viability, and risk assessment, must be addressed. Traditional multi-criteria decision-making (MCDM) approaches do not manage these uncertainties well. This article presents a novel assessment framework based on new Schweizer-Sklar (SS) aggregation operations and the q-rung picture fuzzy hypersoft set (q-RPFHSS) to tackle this problem. These operators enhance decision-making accuracy by modeling sub-parameterized uncertainties. The proposed method is applied to rank five cities for the deployment of ITS, showing excellent flexibility and precision compared to existing methods. Comparative analyses illustrate the robustness of the proposed approach in strategic decision-making for ITS, ensuring a more reliable and adaptive evaluation process.

关键词： Media Access Control   Protocols   Wireless sensor networks   Quality of service   Throughput   Reliability   Directional antennas   Interference   Synchronization   Computer science   MAC   multichannel   directional sensor network   regret matching  

摘要： The performance of wireless networks has continuously improved using Directional Wireless Sensor Networks (DSNs). DSN achieves this by enhancing data sensing accuracy, maximizing spectrum use, decreasing interference and collisions, and lengthening the network's operating lifetime. Within the domain of Directional Sensor Networks (DSNs), the primary difficulties are coordinating transmission and reception possibilities for a variety of sender-receiver pairs and offering traffic priority-aware channel access. To improve the quality and efficiency of multi-channel communication in DSNs, this research presents a quality-aware Directional MAC, Q-DMAC a novel medium access control protocol. The novelty of our protocol lies in its ability to leverage multi-channel resources effectively, enabling reuse through nodes equipped with directional antennas while ensuring the Quality of Service (QoS). Proposed Q-DMAC assigns a higher number of slots inside a data window to nodes carrying more packets waiting to be sent and prioritizes medium access to nodes carrying a significant amount of data packets. Also by employing regret matching of game theory Q-DMAC selects channels resulting in reduced channel switching cost as well as computation cost. Simulation results demonstrate that Q-DMAC outperforms existing protocols such as CAR MAC, 2D-CMD MAC, and DCDS-MAC, achieving up to 20% higher throughput, 15% lower latency, 12% better packet delivery ratio, 25% longer network lifetime, and 15% reduced overhead.

关键词： Databases   Systematic literature review   Machine learning   Art   Visualization   Photonics   Machine learning algorithms   Libraries   Cryptography   Computer science   Golden ratio   phi   phi in computing   Fibonacci number   Golden Section   Phi applications  

摘要： The Golden Ratio (Phi ) has intrigued researchers for centuries due to its widespread presence in both nature and science. This article systematically examines Phi 's diverse applications in computing, focusing on its role in enhancing visual appeal, optimizing algorithms, system design, and influencing fields such as biometrics, machine intelligence, and engineering. Through a systematic review of recent studies, this work aims to identify how Phi can improve computational efficiency across various computing domains. The methodology involves a comprehensive analysis of cutting-edge research sourced from 12 reputable digital platforms to explore Phi 's potential. This study highlights the versatility of Golden Ratio, as its applications and relationships were found to account for 51% and 49%, respectively, across a total of 51 selected studies from 27 countries spanning six continents. The review also categorizes findings by their specific applications and contexts, providing valuable insights into Phi 's impact on mathematics, cryptography, search algorithms, machine learning, artificial intelligence, photonics, natural sciences, system design, power engineering, robotics, and practical human life. This study opens promising avenues for future research on the applications of Phi in diverse fields of science, engineering, and computing.

关键词： Games   Usability   Education   Serious games   Decision making   Proposals   Mobile handsets   Training   Three-dimensional displays   Sustainable development   Computer science   digital game-based learning   science   serious games   usability  

摘要： In recent times, the use of serious games has significantly advanced in science education, with notable contributions in the design and implementation of game mechanics in immersive environments. This research aims to assess the usability and experience of the serious game ABCCI, identifying both the positive and negative effects generated by its interaction from the perspective of pre-service teachers, considering levels of competence, immersion, tension, flow, and challenge. To evaluate usability, the System Usability Scale (SUS) was adapted, and the Game Experience Questionnaire (GEQ) was used to assess the overall experience. The results indicate a moderate positive correlation between the experience and usability of the serious game. Most pre-service teachers expressed positive views about the frequent use of the ABCCI serious game in science education, which successfully created an engaging and positive experience characterized by a sense of competence, immersion, and positive emotions, with an appropriate level of challenge and low levels of negative affect. Further studies are needed to develop a framework for designing games in science education. ABCCI proves to be a promising tool for managing learning.

关键词： Training   Adaptation models   Overfitting   Speech processing   Entropy   Probability distribution   Computer science   Backpropagation   Standards   Propagation losses   Softmax   classification  

摘要： Softmax with the cross entropy loss is the standard configuration for current neural classification models. The gold score for a target class is supposed to be 1, but it is never reachable under the softmax schema. Such a problem makes the training process continue forever and leads to overfitting. Moreover, the "target-approach-1" training goal forces the model to continuously learn all samples, leading to a waste of time in handling some samples which have already been classified correctly with high confidence, while the test goal simply requires the target class of each sample to hold the maximum score. To solve the above weaknesses, we propose the Adaptive Sparse softmax (AS-Softmax) which designs a reasonable and test-matching transformation on top of softmax. For more purposeful learning, we discard the classes with far smaller scores compared with the actual class during training. Then the model could focus on learning to distinguish the target class from its strong opponents, which is also the great challenge in test. In addition, since the training losses of easy samples will gradually drop to 0 in AS-Softmax, we develop an adaptive gradient accumulation strategy based on the masked sample ratio to speed up training. We verify the proposed AS-Softmax on a variety of text multi-class, text multi-label, text token classification, image classification and audio classification tasks with class sizes ranging from 5 to 5000+. The results show that AS-Softmax consistently outperforms softmax and its variants, and the loss of AS-Softmax is remarkably correlated with classification performance in validation. Furthermore, adaptive gradient accumulation strategy can bring about 1.2x training speedup comparing with the standard softmax while maintaining classification effectiveness.

关键词： Students  

摘要： This paper discusses the application effect of mixed teaching method in improving the scientific research and innovation ability of this kind of students. Through the analysis, the authors find that the mixed teaching method can significantly stimulate students' innovative thinking, strengthen the integration of theory and practice, and improve students' teamwork and communication skills. The study uses the empirical analysis method to collect data, such as online learning duration, offline classroom participation, basic knowledge level, and learning attitude of students, and measures the linear relationship between independent variables and dependent variables (scientific research innovation ability) through the Pearson correlation coefficient. The results show that online learning duration, offline classroom participation, and learning attitude have strong positive correlations with scientific research innovation ability. © 2025 IGI Global. All rights reserved.